CN115958586A - Component abnormality monitoring method, electronic device, and storage medium - Google Patents
Component abnormality monitoring method, electronic device, and storage medium Download PDFInfo
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Abstract
The application provides an anomaly monitoring method for components, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring data to be detected of the component; extracting characteristic data from the data to be detected; performing principal component analysis on the feature data to obtain statistic; and monitoring whether the component is abnormal or not according to the statistic and a preset standard value interval. The accuracy and the efficiency of monitoring the abnormity of the components can be improved through the method and the device.
Description
Technical Field
The present disclosure relates to the field of data analysis, and in particular, to an anomaly monitoring method for components, an electronic device, and a storage medium.
Background
At present, because the operation state of a mechanical arm in the production process is invisible, when the mechanical arm has a fault or is about to have a fault, related personnel cannot find the fault in time, so that the fault maintenance is not in time, and the production efficiency and the production quality are seriously influenced.
Disclosure of Invention
In view of the above, it is desirable to provide a method for monitoring abnormality of a component, an electronic device, and a storage medium, which can improve accuracy and efficiency of monitoring abnormality of a component, thereby improving production efficiency.
The application provides an anomaly monitoring method for components, which comprises the following steps: acquiring data to be detected of the component; extracting characteristic data from the data to be detected; performing principal component analysis on the feature data to obtain statistic; and monitoring whether the component is abnormal or not according to the statistic and a preset standard value interval.
In a possible implementation manner, the acquiring data to be measured of the component includes: acquiring sensor data of the component through a sensor; acquiring controller data of the component through a controller; and taking the sensor data and the controller data as the data to be measured.
In a possible implementation manner, the extracting feature data from the data to be tested includes: extracting vibration data and position error data from the sensor data; extracting current data from the controller data; the vibration data, the position error data, and the current data are used as the feature data.
In a possible implementation manner, the performing principal component analysis on the feature data to obtain statistics includes: extracting a plurality of first principal components of the vibration data through a principal component analysis algorithm; extracting a plurality of second principal components of the position error data by the principal component analysis algorithm; extracting a plurality of third principal components of the current data by the principal component analysis algorithm; calculating the statistics from the plurality of first principal components, the plurality of second principal components, and the plurality of third principal components.
In one possible implementation, the calculating the statistics from the plurality of first principal components, the plurality of second principal components, and the plurality of third principal components includes: calculating a T2 statistic value according to the plurality of first principal components, the plurality of second principal components and the plurality of third principal components; calculating to obtain SPE statistical values according to the plurality of first principal components, the plurality of second principal components and the plurality of third principal components; and taking the T2 statistic value and the SPE statistic value as the statistic.
In a possible implementation manner, the monitoring whether the component is abnormal according to the statistical quantity and a preset standard value interval includes: determining a first monitoring result according to the T2 statistic value and a preset first standard value interval; determining a second monitoring result according to the SPE statistical value and a preset second standard value interval; and monitoring whether the component is abnormal or not according to the first monitoring result and the second monitoring result.
In a possible implementation manner, the determining a first monitoring result according to the T2 statistic and the preset first standard value interval includes: judging whether the T2 statistic value is within the preset first standard value interval or not; when the T2 statistic value is within the preset first standard value interval, determining that the first monitoring result is normal; and when the T2 statistic value is not within the preset first standard value interval, determining that the first monitoring result is abnormal.
In a possible implementation manner, the determining whether the component is abnormal according to the first monitoring result and the second monitoring result includes: when each monitoring result in the first monitoring result and the second monitoring result is normal, determining that the component is not abnormal; and when at least one of the first monitoring result and the second monitoring result is abnormal, determining that the component is abnormal.
The application further provides electronic equipment which comprises a processor and a memory, wherein the processor is used for realizing the abnormity monitoring method of the component when executing the computer program stored in the memory.
The application also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the computer program realizes the abnormal monitoring method of the component.
The utility model discloses an unusual monitoring method of components and parts and relevant equipment, after the data that awaits measuring that acquires components and parts, through extracting the characteristic data from the data that awaits measuring of components and parts, will influence the main data of components and parts and draw out, it is right through utilizing principal component analysis algorithm the characteristic data carries out principal component analysis and obtains statistics, can reduce the dimension to the dimension of characteristic data to obtain the lower statistics of dimension, finally according to statistics and the regional monitoring of predetermined standard value whether components and parts exist unusually, because statistics is the lower a small amount of data of dimension, therefore carry out the unusual monitoring of components and parts based on statistics, reduced the complexity of data calculation, the reduction of data complexity helps improving the efficiency of unusual analysis. The component devices are rapidly and effectively monitored abnormally, so that the components with faults are timely monitored, the abnormal component devices are removed, and the production quality of products is improved in an auxiliary mode.
Drawings
Fig. 1 is a schematic structural diagram of an electronic device according to an abnormality monitoring method for a component provided in an embodiment of the present application.
Fig. 2 is a flowchart of an abnormality monitoring method for a component according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in detail below with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, fig. 1 is a schematic view of an electronic device according to an embodiment of the present application. Referring to fig. 1, the electronic device 1 includes, but is not limited to, a memory 11 and at least one processor 12, which may be connected via a bus or directly.
The electronic device 1 may be a computer, a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), or other devices installed with an application program. Those skilled in the art will appreciate that the schematic diagram 1 is merely an example of the electronic device 1, and does not constitute a limitation of the electronic device 1, and may include more or less components than those shown, or combine some components, or different components, for example, the electronic device 1 may further include an input-output device, a network access device, a bus (e.g., 13 shown in fig. 1), and the like.
Fig. 2 is a flowchart of a method for monitoring abnormality of a component according to a preferred embodiment of the present invention. The abnormality monitoring method for the components is applied to the electronic device 1. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs. In this embodiment, the method for monitoring abnormality of a component includes:
and S21, acquiring data to be detected of the component.
In one embodiment of the present application, the component may be a robotic arm (UR). The UR mechanical arm is mainly applied to production workshops and can be used for assembling, painting, screwing, labeling, injection molding and welding. The UR mechanical arm has the characteristics of simple programming, flexible deployment, quick installation, high safety and the like. Acquiring data to be tested of the component may include: acquiring sensor data of the UR mechanical arm through a sensor; acquiring controller data of the UR mechanical arm through a controller; and taking the sensor data and the controller data as the data to be measured.
In the specific implementation process, the first-stage reactor,
(1) Aiming at the characteristics of high hardness and difficult grooving of the UR mechanical arm, an ultrasonic sensor is arranged on a shaft joint motor and a clamp jig machine component of the UR mechanical arm in a sticking installation mode. And acquiring sensor data of the UR mechanical arm in real time through the ultrasonic sensor, such as the movement speed of the UR mechanical arm, the angle of the UR mechanical arm and the distance between the UR mechanical arm and a target point. The ultrasonic sensor sends the sensor data to the data acquisition card, the data acquisition card segments the sensor data, and non-standard data and redundant data can be removed through data segmentation to obtain useful data. And then, the data acquisition card sends the segmented sensor data to the electronic equipment. Since the UR robot is configured by a plurality of single axes, data of each single axis can be acquired, and the following embodiments may be data processing and analysis performed for each single axis.
(2) And acquiring controller data of the UR mechanical arm through a controller carried by the UR mechanical arm, wherein the controller refers to a master device for controlling the starting, speed regulation, braking and reversing of the motor by changing the wiring of a main circuit or a control circuit and changing the resistance value in the circuit according to a preset sequence, and the controller data comprises various current data, such as the resistance value and the current direction. And the controller sends the controller data to a data acquisition card, and the data acquisition card divides the controller data. And then, the data acquisition card sends the segmented controller data to the electronic equipment.
In an embodiment of the application, compared with a traditional strain sensor, the ultrasonic sensor has the advantages of small size, quick response, wide measurement frequency range, high linearity, no need of an external power supply and the like. By collecting the sensor data and the controller data, the accuracy and the universality of the data are improved, and the subsequent data analysis can be more accurate.
And S22, extracting characteristic data from the data to be detected.
In practical applications, the sensor data and the controller data often include a large number of redundant data types, and therefore, in order to improve the efficiency and accuracy of the subsequent data analysis, it is necessary to extract representative data from the data to be analyzed.
In one embodiment of the present application, the representative data includes: vibration data, position error data, and current data. The extracting of the feature data from the data to be tested comprises: extracting the vibration data and the position error data from the sensor data; extracting the current data from the controller data; the vibration data, the position error data, and the current data are used as the feature data.
In specific implementation, three types of sample data are obtained, wherein the three types of sample data are a vibration data sample, a position error data sample and a current data sample. Sequentially extracting first data from the sensor data, calculating a plurality of first distances between the first data and each data in the vibration data sample and a plurality of second distances between the first data and each data in the position error data sample, taking a first average value of the first distances, taking the first data with the first average value smaller than a preset first average value threshold value as the vibration data, taking the second average value of the second distances, and taking the first data with the second average value smaller than a preset second average value threshold value as the position error data until all the first data in the sensor data are extracted. Then, second data are sequentially extracted from the controller data, a plurality of third distances between the second data and each data in the current data samples are calculated, the plurality of third distances are averaged, and the third data with the average value smaller than a preset third average threshold value are used as the current data until all third data in the controller data are extracted.
By extracting the characteristic data in the data to be detected, the main data influencing the UR mechanical arm is extracted, and the efficiency and accuracy of subsequent data analysis can be improved.
And S23, performing principal component analysis on the feature data to obtain statistic.
In an embodiment of the present application, the calculating statistics of the feature data by the principal component analysis algorithm includes: extracting a plurality of first principal components of the vibration data through a principal component analysis algorithm; extracting a plurality of second principal components of the position error data by a principal component analysis algorithm; extracting a plurality of third principal components of the current data by a principal component analysis algorithm; calculating the statistics from the plurality of first principal components, the plurality of second principal components, and the plurality of third principal components.
Said computing said statistics from said plurality of first principal components, said plurality of second principal components, and said plurality of third principal components comprises: calculating a T2 statistic value according to the plurality of first principal components, the plurality of second principal components and the plurality of third principal components; calculating to obtain SPE statistical values according to the plurality of first principal components, the plurality of second principal components and the plurality of third principal components; and taking the T2 statistic value and the SPE statistic value as the statistic.
In specific implementation, the vibration data is subjected to standardization processing to obtain first standardized data. Performing dimensionality reduction on the first normalized data through a Principal Component Analysis (PCA) algorithm to obtain a plurality of first Principal components, calculating a first covariance matrix of the first normalized data, further calculating a first eigenvalue and a first eigenvector of the first covariance matrix, obtaining a contribution rate of each first eigenvalue, and taking the first Principal component of which the contribution rate is greater than a preset first threshold value as the plurality of first Principal components. And carrying out standardization processing on the position error data to obtain second standardized data. And performing dimensionality reduction on the second normalized data through the PCA algorithm to obtain a plurality of second principal components, calculating a second covariance matrix of the second normalized data, further calculating second eigenvalues and second eigenvectors of the second covariance matrix, obtaining the contribution rate of each second eigenvalue, and taking the second principal components with the contribution rates larger than a preset second threshold value as the plurality of first principal components. And carrying out standardization processing on the current data to obtain third standardized data. And performing dimensionality reduction on the third standardized data through the PCA algorithm to obtain a plurality of third principal components, calculating a third covariance matrix of the third standardized data, further calculating a third eigenvalue and a third eigenvector of the third covariance matrix, obtaining the contribution rate of each third eigenvalue, and taking the third principal components with the contribution rates larger than a preset third threshold value as the plurality of third principal components. Further, the plurality of first principal components, the plurality of second principal components, and the plurality of third principal components may be input to a PCA algorithm model, and the T2 statistic and the SPE statistic may be output.
The principal component analysis algorithm is utilized to reduce the dimension of a large amount of data into a small amount of data, so that the complexity of the data is reduced, and the efficiency of data anomaly analysis can be improved.
And S24, monitoring whether the component is abnormal or not according to the statistics and a preset standard value interval.
When the UR robot is normal, the load is in the range of 5 kg to 9 kg, that is, when the UR robot is normal, the statistic is in a standard value range. The standard value interval can be obtained by collecting data of a normal load-bearing interval.
In one embodiment of the application, first normal data of a UR manipulator bearing a load of 5 kg is obtained, and a first T2 standard value and a first SPE standard value of the first normal data are obtained by using a principal component analysis model. And then, acquiring second normal data of the UR mechanical arm with the load of 9 kg, and obtaining a second T2 standard value and a second SPE standard value of the second normal data by using a principal component analysis model. And determining a preset first standard value interval according to the first T2 standard value and the second T2 standard value, for example, the first T2 standard value is 300, the second T2 standard value is 700, and thus determining the preset first standard value interval to be (300, 700). And determining a preset second standard value interval according to the first SPE standard value and the second SPE standard value, wherein the first SPE standard value is 40, and the second SPE standard value is 90, so that the preset second standard value interval is (40, 90).
In an embodiment of the application, the determining whether the component has an abnormality according to the statistical quantity and a preset standard value interval includes: determining a first monitoring result according to the T2 statistic value and a preset first standard value interval; determining a second monitoring result according to the SPE statistical value and a preset second standard value interval; and monitoring whether the component is abnormal or not according to the first monitoring result and the second monitoring result.
In an embodiment of the application, the determining a first monitoring result according to the T2 statistic and a preset first standard value interval includes: judging whether the T2 statistic value is within the preset first standard value interval or not; when the T2 statistic value is within the preset first standard value interval, determining that the first monitoring result is normal; and when the T2 statistic value is not in the preset first standard value interval, determining that the first monitoring result is abnormal.
In an embodiment of the present application, the determining a second monitoring result according to the SPE statistic and a preset second standard value interval includes: when the SPE statistical value is located in the preset second standard value interval, determining that the second monitoring result is normal; and when the SPE statistical value is not located in the preset second standard value interval, determining that the second monitoring result is abnormal.
In an embodiment of the present application, determining whether the component is abnormal according to the first monitoring result and the second monitoring result includes: when each monitoring result in the first monitoring result and the second monitoring result is normal, determining that the component is not abnormal; and when at least one of the first monitoring result and the second monitoring result is abnormal, determining that the component is abnormal. Specifically, when the first monitoring result is that abnormality exists and the second monitoring result is that abnormality does not exist, it is determined that the UR robot arm is abnormal, when the first monitoring result is that abnormality does not exist and the second monitoring result is that abnormality exists, it is determined that the UR robot arm is abnormal, and when the first monitoring result is that abnormality exists and the second monitoring result is that abnormality exists, it is determined that the UR robot arm is abnormal.
By monitoring the data of the UR mechanical arm in real time and analyzing the data, the abnormal condition of the UR mechanical arm can be pre-warned, a solution can be timely adopted, the trouble is reduced, and the production efficiency of a workshop can be improved. By adopting the method, the state of the UR mechanical arm can be monitored for a long time.
As an optional implementation, the method further comprises: and drawing a statistic walking graph according to the statistic and the corresponding time point.
In an embodiment of the application, by drawing the statistic walking graph, the state of the UR manipulator can be predicted, so that problems can be found in time.
Referring to fig. 1, in the present embodiment, the memory 11 may be an internal memory of the electronic device 1, that is, a memory built in the electronic device 1. In other embodiments, the memory 11 may also be an external memory of the electronic device 1, that is, a memory externally connected to the electronic device 1.
In some embodiments, the memory 11 is used for storing program codes and various data, and realizes high-speed and automatic access to programs or data during the operation of the electronic device 1.
The memory 11 may include random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
In one embodiment, the Processor 12 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any other conventional processor or the like.
The program code and various data in the memory 11 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the processes in the methods of the embodiments, such as the abnormality monitoring method for the components, may also be implemented by instructing the related hardware through a computer program, which may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the methods may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic diskette, an optical disk, a computer Memory, a Read-Only Memory (ROM), etc.
It is understood that the above described division of modules is a logical division, and there may be other divisions when the module is actually implemented. In addition, functional modules in the embodiments of the present application may be integrated into the same processing unit, or each module may exist alone physically, or two or more modules are integrated into the same unit. The integrated module can be realized in a hardware mode, and can also be realized in a mode of hardware and a software functional module.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.
Claims (10)
1. An abnormality monitoring method for a component, characterized by comprising:
acquiring data to be detected of the component;
extracting characteristic data from the data to be detected;
performing principal component analysis on the characteristic data to obtain statistic;
and monitoring whether the component is abnormal or not according to the statistic and a preset standard value interval.
2. The method for monitoring abnormality of a component according to claim 1, wherein the acquiring data to be measured of the component includes:
acquiring sensor data of the component through a sensor;
acquiring controller data of the component through a controller;
and taking the sensor data and the controller data as the data to be measured.
3. The method for monitoring abnormality of a component according to claim 2, wherein the extracting of the feature data from the data to be measured includes:
extracting vibration data and position error data from the sensor data;
extracting current data from the controller data;
the vibration data, the position error data, and the current data are taken as the feature data.
4. The method for monitoring abnormality of component parts according to claim 3, wherein the performing principal component analysis on the feature data to obtain statistical quantity includes:
extracting a plurality of first principal components of the vibration data through a principal component analysis algorithm;
extracting a plurality of second principal components of the position error data by the principal component analysis algorithm;
extracting a plurality of third principal components of the current data by the principal component analysis algorithm;
calculating the statistics from the plurality of first principal components, the plurality of second principal components, and the plurality of third principal components.
5. A component abnormality monitoring method according to claim 4, wherein said calculating the statistical amount based on the plurality of first principal components, the plurality of second principal components, and the plurality of third principal components includes:
calculating a T2 statistic according to the plurality of first principal components, the plurality of second principal components and the plurality of third principal components;
calculating to obtain SPE statistical values according to the plurality of first principal components, the plurality of second principal components and the plurality of third principal components;
and taking the T2 statistic value and the SPE statistic value as the statistic.
6. The method for monitoring the abnormality of the component according to claim 5, wherein the monitoring whether the component has the abnormality or not according to the statistic and a preset standard value interval includes:
determining a first monitoring result according to the T2 statistic and a preset first standard value interval;
determining a second monitoring result according to the SPE statistical value and a preset second standard value interval;
and monitoring whether the component is abnormal or not according to the first monitoring result and the second monitoring result.
7. The method for monitoring abnormality of components according to claim 6, wherein the determining a first monitoring result according to the T2 statistic and the preset first standard value interval includes:
judging whether the T2 statistic value is within the preset first standard value interval or not;
when the T2 statistic value is within the preset first standard value interval, determining that the first monitoring result is normal;
and when the T2 statistic value is not within the preset first standard value interval, determining that the first monitoring result is abnormal.
8. The method for monitoring abnormality of a component according to claim 7, wherein the determining whether the component has abnormality according to the first monitoring result and the second monitoring result includes:
when each monitoring result in the first monitoring result and the second monitoring result is normal, determining that the component is not abnormal;
and when at least one of the first monitoring result and the second monitoring result is abnormal, determining that the component is abnormal.
9. An electronic device, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the method for monitoring abnormality of the component as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and the at least one instruction when executed by a processor implements the method for monitoring abnormality of the component as claimed in any one of claims 1 to 8.
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| CN202111172845.5A CN115958586A (en) | 2021-10-08 | 2021-10-08 | Component abnormality monitoring method, electronic device, and storage medium |
| TW110139385A TWI795048B (en) | 2021-10-08 | 2021-10-22 | Component abnormality monitoring method, electronic equipment, and storage medium |
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| EP1955830B1 (en) * | 2007-02-06 | 2014-04-09 | Abb Research Ltd. | A method and a control system for monitoring the condition of an industrial robot |
| CN112763678A (en) * | 2020-12-30 | 2021-05-07 | 佛山科学技术学院 | PCA-based sewage treatment process monitoring method and system |
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